Instructions to use miittnnss/diffusion-faces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use miittnnss/diffusion-faces with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("miittnnss/diffusion-faces", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 674d5836529e0da6272fdf66a23a86864510d3cc68a9392ed3a7f8e930ad02a3
- Size of remote file:
- 400 kB
- SHA256:
- d56e613854034bd640dd1140b5d6336887e7df09221c872b5a8d2a8c66a6f707
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.